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Applied Sciences
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Information and Communication Technologies (ICTs) in Food and Environment
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Information and Communication Technologies (ICTs) in Food and Environment

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 December 2021) | Viewed by 3312

Special Issue Editor

Prof. Dr. Theodore A. Tsiligiridis

E-Mail Website
Guest Editor
Division of Informatics, Department of Agricultural Economy and Rural Development, School of Food, Biotechnology and Development, Agricultural University of Athens, 75 Iera Odos, 11855 Athens, Greece
Interests: computer networking; intelligent networks and e-services; precision agriculture and smart farming; remote sensing in farming systems; environmental information technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

So far, substantial progress has been made in research, development, and the use of new Information and Communication Technologies (ICTs) in the Food and Environmental sciences. Innovative experiments are used as key instruments to mature ICTs, meeting end-user needs, to demonstrate agro-robotics solutions, increasing technology integration, as well as to test and improve the network quality and services. Further, recent satellite imagery advances combined with ground-based data and spatial mapping tools can make an enormous difference to agricultural decision making at global, national, and local levels, by providing more timely and accurate information, particularly for smallholder farming systems. Clearly, much potential is seen in real case applications in the combined fields of sensor networking, e-monitoring and decision making, precision agriculture and robotics in smart farming, machine learning techniques, image processing and identification, as well as remote sensing for land crop type mapping, and food security assessment.

The proposed Special Issue will leverage these promising developments to share the latest research but also real-life environments and large-scale developments, coming from the intersection of digital technologies with food and environmental sciences. Articles covering but not limited to recent research on the following topics are invited to this Special issue.

  • long-range, low-energy, and low-cost sensor network technologies;
  • precision Agriculture (PA) and UAV applications;
  • robots in the agri-food sector;
  • smart technologies and the environment;
  • e-monitoring and decision-making IPM (Integrated Pest Management) applications;
  • algorithms and applications on insect identification;
  • machine learning applications in agriculture and food security;
  • applications on crop yield mapping and monitoring;
  • crop type/land mapping methods for smallholder farming systems.

Prof. Theodore A. Tsiligiridis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (1 paper)

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Research

12 pages, 779 KiB  
Article
Learning Attention-Aware Interactive Features for Fine-Grained Vegetable and Fruit Classification
by Yimin Wang, Zhifeng Xiao and Lingguo Meng
Appl. Sci. 2021, 11(14), 6533; https://doi.org/10.3390/app11146533 - 16 Jul 2021
Cited by 2 | Viewed by 2433
Abstract
Vegetable and fruit recognition can be considered as a fine-grained visual categorization (FGVC) task, which is challenging due to the large intraclass variances and small interclass variances. A mainstream direction to address the challenge is to exploit fine-grained local/global features to enhance the [...] Read more.
Vegetable and fruit recognition can be considered as a fine-grained visual categorization (FGVC) task, which is challenging due to the large intraclass variances and small interclass variances. A mainstream direction to address the challenge is to exploit fine-grained local/global features to enhance the feature extraction and representation in the learning pipeline. However, unlike the human visual system, most of the existing FGVC methods only extract features from individual images during training. In contrast, human beings can learn discriminative features by comparing two different images. Inspired by this intuition, a recent FGVC method, named Attentive Pairwise Interaction Network (API-Net), takes as input an image pair for pairwise feature interaction and demonstrates superior performance in several open FGVC data sets. However, the accuracy of API-Net on VegFru, a domain-specific FGVC data set, is lower than expected, potentially due to the lack of spatialwise attention. Following this direction, we propose an FGVC framework named Attention-aware Interactive Features Network (AIF-Net) that refines the API-Net by integrating an attentive feature extractor into the backbone network. Specifically, we employ a region proposal network (RPN) to generate a collection of informative regions and apply a biattention module to learn global and local attentive feature maps, which are fused and fed into an interactive feature learning subnetwork. The novel neural structure is verified through extensive experiments and shows consistent performance improvement in comparison with the SOTA on the VegFru data set, demonstrating its superiority in fine-grained vegetable and fruit recognition. We also discover that a concatenation fusion operation applied in the feature extractor, along with three top-scoring regions suggested by an RPN, can effectively boost the performance. Full article
(This article belongs to the Special Issue Information and Communication Technologies (ICTs) in Food and Environment)
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